{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "from neuron import h"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from CA3.library import christoph2007\n",
      "mycell = christoph2007()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<H3>Synapse</H3>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mysyn = h.Exp2Syn(0.5, sec=mycell.soma)\n",
      "mysyn.tau1 = 0.2 # tau on in ms\n",
      "mysyn.tau2 = 2.5 # tau delay in ms\n",
      "mysyn.e = 0.0    # reversal potential"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<H3>NetStim: presynaptic stimuli</H3>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stim = h.NetStim() # arbitrary location\n",
      "stim.interval = 50\n",
      "stim.number = 10\n",
      "stim.start = 40\n",
      "stim.noise = 0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nt = h.NetCon(stim, mysyn)\n",
      "nt.weight[0] = 300e-6\n",
      "nt.delay = 0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "VC_patch = h.SEClamp(0.5, sec=mycell.soma)\n",
      "VC_patch.rs = 0.1 # ms\n",
      "VC_patch.amp1 = -70 # mV\n",
      "VC_patch.dur1 = 1000 # in ms"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# prepare simulation\n",
      "h.load_file('stdrun.hoc')\n",
      "h.tstop = 1000 # ms\n",
      "h.v_init = -70\n",
      "\n",
      "time = h.Vector()\n",
      "time.record(h._ref_t)\n",
      "\n",
      "i_soma = h.Vector()\n",
      "i_soma.record(VC_patch._ref_i)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "1.0"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "h.run()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "0.0"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(time, np.array(i_soma)*1000, 'r'); # transform to pA\n",
      "plt.xlabel('Time (ms)');\n",
      "plt.ylabel('Current (pA)');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZkAAAE7CAYAAAAPcfwrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLPF+yuHYMgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3add410>"
       ]
      }
     ],
     "prompt_number": 9
    }
   ],
   "metadata": {}
  }
 ]
}